Apparatus that incorporate imaging techniques for constructing images from image data elements are well known. The simplest of these devices are character scanners that use character recognition techniques to "read" documents.
Character recognition devices analyze binary value images. Binary value images are made from data elements which can only have one of two values. When the image data element is at one value it represents the background and when it is at the second value it represents a portion of a character. Data elements having erroneous data values result from paper imperfections, smeared characters, or the like and are introduced when the document is scanned or "read" by the device. This means that data elements that should have the background value have the character portion value instead and vice-versa.
When the imaging device, such as a computer, attempts to match a known character pattern to the data elements having character portion values in the scanned image and a significant number of the elements have erroneous values, it fails to determine the character correctly and thus, cannot convert it into a data format, such as ASCII, for storage. Techniques have been developed by which the imaging device can analyze the image data elements and substantially eliminate or correct the erroneous data elements. The computer can then efficiently match the corrected data with stored patterns to accurately identify the characters for prompt data storage.
While character recognition techniques are useful in evaluating binary value images, such techniques are generally ineffective on gray scale images. Gray scale images are produced by data elements or pixels that can have one of several different data values. One end of the gray scale value range represents a black pixel while the other end of the gray scale value range represents a white pixel. The values in between represent slight gradations from one end of the gray scale value range to the other. Typically, a gray scale data element numerically ranges from 0 to 255.
Gray scale images are produced by receptor elements that convert radiant energy reflected from an object into analog electrical signals. These analog signals are digitized by A/D converters into gray scale values that correspond to the intensity or brightness of the radiant energy on the receptors. Sometimes faulty receptors or converters produce data elements having gray scale values that are not accurate. These erroneous data elements are sometimes called noise.
Image identification with gray scale images is more difficult because the object from which the sensory image data is received may have distinct regions within the periphery of the object. This means that the device not only must distinguish the object from the background but must also distinguish different regions of the object itself. Although the gray scale range is wide enough so the different regions and their boundaries are distinguishable by the imaging device when there is sufficient light, poor lighting conditions may cause the data elements to assume values within a narrow spectrum of the data range. Because the features are made of data elements that are very close in gray scale value to the elements of other features nearby, the boundaries between features are difficult to differentiate from one another. If the imaging device adjusts to these conditions by detecting edges at smaller differences in gray scale values between proximate pixels, it begins to detect edges from the inaccurate data elements caused by the faulty components that are slightly different than the accurately produced values nearby. Thus, the device produces an image with features not really present in the object and the identification of the object becomes difficult, if not impossible, by present methods.
One known gray scale imaging technique used to enhance the data elements in a gray scale image determines the energy of the image data elements and compares it to a constrained error function to correct data elements in the image. This technique has proven successful for blurred images in good lighting conditions. Blurred images are a somewhat simpler case than images produced in poor light since they have erroneous data elements that are generally uniformly distorted about the correct value. Additionally, while a feature may be blurred it is not obscured. Noisy image data resulting from poor lighting conditions are not uniformly distorted and the shadows created by the poor light tends to hide features. These differences make the blurred image resolving techniques marginally useful for poor lighting conditions. What is needed is a method for correcting or enhancing data elements generated from poor lighting conditions to reveal features within shadows.
Poor lighting conditions also apply to medical images formed by radiographic or x-ray techniques. Poor lighting in such applications arise from radiation input levels focused on one area of the body that are sufficient to provide image details for the region of focus but are insufficient to provide imaging details for neighboring areas. One way to eliminate this problem is to increase the radiation level, however, this may harm tissue in the focus area from radiation overexposure. What is needed is a method for enhancing the image data without increasing the radiation exposure of the patient.
One method for enhancing gray scale values is described in U.S. Pat. No. 4,941,190. The apparatus of that patent implements a non-sharp mask filter to enhance the data elements. A number of pixels centered about a selected pixel are retrieved and a center deviation is computed from the differences in gray scale values between each pixel and the selected pixel. The summation of these differences are used to determine a overall contrast for the pixels retrieved by the mask and the selected pixel value. Typically, the greater the contrast, the larger the correction or enhancement made to the element.
When a noisy element differs by several levels from the surrounding elements having approximately the same values, a large enough contrast is produced to cause the selected pixel value to be modified to further increase the difference that is needed to bring out an edge or boundary between features. For the noisy element, the correction makes the noise more prominent and creates a feature where one does not exist. What is needed is an imaging technique that rejects for correction or enhancement noisy or erroneous data elements that exist in an area with data elements having approximately the same gray scale values.
Another known gray scale imaging technique examines edges within an image and extrapolates the data elements near the detectable edges to extend them for further pattern recognition. This method uses a Laplacian gradient of the Gaussian function to evaluate zero crossings of the resultant second order derivative or, alternatively, a Gabor transform of the Gaussian function. These techniques are successful if there are a sufficient number of edges in the original data that the extrapolated edges from them correspond to stored patterns used for recognition. This technique, however, cannot accurately enhance or correct the image data elements underlying the extrapolated edge. What is needed is a method of enhancing image data elements proximate a detected edge to reveal edges in the image data.